Shrub encroachment is an important ecological issue that is increasingly receiving global attention in arid and semiarid grasslands. Monitoring the spatial distribution of encroached shrub aboveground biomass (AGB) is critical for ecological conservation and adaptive ecosystem management. However, the low stature and fine spatial heterogeneity of encroached shrub communities increase difficulties for coarse spatial-resolution satellite images to adequately capture detailed characteristics of individual shrubs. Unmanned aerial vehicle (UAV) can acquire centimeter-level optical images or high-density LiDAR point cloud data, providing an effective means to map encroached shrub AGB spatially explicitly, even at the individual scale. In this study, we first extracted the individual shrubs based on thresholds in normalized difference vegetation index (NDVI) and canopy height model (CHM) using UAV-based multispectral and LiDAR data. For each shrub, we then derived and determined the dominant geometric, spectral, and textural features from the high-resolution multispectral image and the volumetric features from the LiDAR data as predictors of shrub AGB. Finally, we compared the capability of different data sources (UAV-based multispectral image, LiDAR, and their combination) and regression methods (multiple linear, random forest, and support vector regression) to estimate and map the individual shrub AGB in the study area. The volume-based approaches to individual shrub AGB, including global convex hull method, voxel method, and surface differencing method, were also employed using terrestrial laser scanning (TLS) to further calibrate the UAV-based estimation. Our results show that individual shrubs can be accurately extracted based on the threshold method with an overall classification accuracy of 91.8%. The UAV-based AGB estimation suggests that the textural feature, the sum of contrast metric within the individual shrub canopy, is the most important predictor of individual shrub AGB, followed by volumetric, geometric and spectral features. Moreover, the high-resolution multispectral image shows greater potential (R2 = 0.83, RMSE = 106.46 g) than LiDAR (R2 = 0.77, RMSE = 123.33 g) in the estimation of individual shrub AGB, and their combination can only slightly improve the estimation accuracy (R2 = 0.86, RMSE = 101.97 g). Our results also show that TLS-derived volume based on the surface differencing method obtained the best prediction accuracy of individual shrub AGB (R2 = 0.91, RMSE = 79.98 g), and can be used as an alternative of destructive harvesting. This study provides a new insight for quantifying and mapping individual shrub AGB using UAV-based optical sensors and TLS without destructive harvesting in arid and semiarid grasslands.
A high-speed debris flow sliding into a reservoir can cause a huge disaster. Consequently, predicting landslide movement accurately and its potential interaction with water is crucial. This paper developed a computational model based on a two–layer two–phase material point method (MPM) to simulate surge waves generated by granular landslides on an erodible slope. By comparing granular landslide on a rigid and erodible slope, the effect of the slope erodibility on the process of landslide movement and the waves generated is investigated. The model takes full account of the large deformations, fluidisation and settlement of granular material in soil–water interactions. The numerical model is validated by comparing the simulated results with experimental data. The influences of internal friction angle, density, elastic modulus, Poisson ratio and dilatancy angle on wave height are also studied. The validated model was then used to investigate the surge waves generated by dry and saturated granules sliding along a rigid and erodible slope. The results show that both the erodible slope and saturated granular slide can increase the first wave crest height generated by the landslide.
Point cloud understanding is an inherently challenging problem because of the sparse and unordered structure of the point cloud in the 3D space. Recently, Contrastive Vision-Language Pre-training (CLIP) based point cloud classification model i.e. PointCLIP has added a new direction in the point cloud classification research domain. In this method, at first multi-view depth maps are extracted from the point cloud and passed through the CLIP visual encoder. To transfer the 3D knowledge to the network, a small network called an adapter is fine-tuned on top of the CLIP visual encoder. PointCLIP has two limitations. Firstly, the point cloud depth maps lack image information which is essential for tasks like classification and recognition. Secondly, the adapter only relies on the global representation of the multi-view features. Motivated by this observation, we propose a Pretrained Point Cloud to Image Translation Network (PPCITNet) that produces generalized colored images along with additional salient visual cues to the point cloud depth maps so that it can achieve promising performance on point cloud classification and understanding. In addition, we propose a novel viewpoint adapter that combines the view feature processed by each viewpoint as well as the global intertwined knowledge that exists across the multi-view features. The experimental results demonstrate the superior performance of the proposed model over existing state-of-the-art CLIP-based models on ModelNet10, ModelNet40, and ScanobjectNN datasets.
In this paper, we propose a Low Voltage Trigger Silicon Controlled Rectifier (LVTSCR) device based on LVTSCR with Deep N-type well (DNW) isolation, realized by embedding reverse diode into the device, operating an additional parasitic PNP bipolar junction transistor (BJT) in the anode terminal and adding an extra N-well(NW)region. The proposed electrostatic discharge (ESD) protection device was developed through a 0.18μm Bipolar CMOS-DMOS (BCD) process. According to the analysis of TCAD simulation and measurement results of transmission line pulse (TLP) testing system, the LVTSCR embedded reverse diode reduces the slope of the reverse TLP I-V curve, which can effectively reduce the reverse conduction resistance. On the basis of the LVTSCR embedded reverse diode, when the additional parasitic PNP BJT operates in the anode terminal, the performance of this device is basically not affected. But the holding voltage of the LVTSCR embedded reverse diode can be improved by inserting additional NW with different widths in the P-well.
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